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Enregistrement W2945616753 · doi:10.11124/jbisrir-d-19-00133

Up, dressed and moving: how nurses are employing evidence to transform patient care

2019· editorial· en· W2945616753 sur OpenAlexaboutno aff
Bridie Kent

Notice bibliographique

RevueThe JBI Database of Systematic Reviews and Implementation Reports · 2019
Typeeditorial
Langueen
DomaineMedicine
ThématiqueHospital Admissions and Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésParaphraseHealth carePublic relationsScale (ratio)NursingSimple (philosophy)PsychologyMedicinePolitical scienceLawComputer science

Résumé

récupéré en direct d'OpenAlex

To paraphrase Benjamin Disraeli, the secret of success is to be ready when your opportunity comes. In 2019, the International Council of Nurses (ICN) stated that “nurses are essential in transforming health care and health systems such that no person is left behind, without access to care or impoverished because of their need for health care.”1(para.1) Across the world, examples of transformation by nurses can be found that truly make a difference to people's lives. These differences may be large or small in scale, but regardless, for the people concerned, they are life-enhancing. Bringing about change requires people to think and behave differently; this can be quite a challenge. However, by understanding the factors associated with successful implementation of new ideas or practices, we can be far better prepared to act when the opportunity presents and the timing appears right. One idea that has transformed health care and gone viral is #EndPJParalysis.2 The campaign, led by a nurse, Professor Brian Dolan, has become a global social movement embraced by nurses, therapists and medical practitioners, and aims to get patients up, dressed and moving. These simple activities have been found to shorten hospital stays, reduce falls and enhance wellbeing. The campaign has made a significant difference to how we care for people in hospital, with many countries worldwide taking up Professor Dolan's challenge and transforming lives. These ideas were shared with an audience who listened, understood why change was needed, and accepted the challenge by changing their behavior to make a difference. Each year, nurses from across the world acknowledge the contributions to the profession by an early leader in nursing, Florence Nightingale, by celebrating International Nurses Day on May 12, which was Nightingale's birthday. The theme of International Nurses Day for 2019, set by the ICN, addresses the leadership role of nurses by focusing specifically on the challenges related to achieving “Health for all”. This theme is reflected in the Joanna Briggs Institute's vision: “a world in which the best available evidence is used to inform decision making at the point of care to improve health outcomes in communities globally”. Nurses are key players in achieving this, in terms of generating, implementing and embedding evidence that can be used to underpin decision making. In this month's issue of the journal, we see how evidence synthesis is being used to establish the best available evidence for three very different aspects of health care of direct relevance to nursing: handover,3,4 workplace violence5 and compassion fatigue.6 Each of these impacts health in different ways in every part of the world. Nurses working in all types of healthcare settings across the world rely on information being communicated effectively from one team of people to another to enable the delivery of health and social care. Good communication is vital for the effective transfer of information, and evidence indicates it is a critical component of patient safety; however, its importance is often underestimated.7 Practices associated with daily interprofessional handovers and shift-to-shift handovers have changed in recent years, and they are (slowly) becoming more evidence-informed. We have seen more and more evidence of patients actively being included in these communication exchanges, with some informative research producing significant contributions to the body of knowledge in this area by Dawn Stacey et al.8 These researchers from Ottawa, Canada, have developed a decision-making aid for use with patients in an effort to translate evidence into a useful tool for clinicians. In some acute services, bedside discussions and care planning have been introduced, using a team-based, interprofessional approach that is now generating a growing evidence base.9,10 Many of these also involve the patient in this shared decision-making process.11 The culture of care is changing, and this creates opportunities for transformation to occur. Nurses need to respond to the call by the ICN and truly be the voice that leads the changes needed to achieve a world in which no person is left wanting for care. As a profession, we need to call on all nurses to be patient advocates, to use our hard-earned scientific reasoning skills and abilities to transform practice, and take advantage of our large numbers to cascade this vision far and wide.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,476
Score d'incertitude au seuil0,819

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,052
Tête enseignante GPT0,391
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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